8000 docs: add code samples in the `udf` API docstring by shobsi · Pull Request #1632 · googleapis/python-bigquery-dataframes · GitHub
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docs: add code samples in the udf API docstring #1632

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shobsi merged 1 commit into from
Apr 21, 2025
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docs: add code samples in the udf API docstring #1632

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67 changes: 64 additions & 3 deletions bigframes/session/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1447,17 +1447,78 @@ def udf(
packages: Optional[Sequence[str]] = None,
):
"""Decorator to turn a Python user defined function (udf) into a
BigQuery managed function.
[BigQuery managed user-defined function](https://cloud.google.com/bigquery/docs/user-defined-functions-python).

.. note::
The udf must be self-contained, i.e. it must not contain any
references to an import or variable defined outside the function
body.

.. note::
Please have following IAM roles enabled for you:
Please have BigQuery Data Editor (roles/bigquery.dataEditor) IAM
role enabled for you.

* BigQuery Data Editor (roles/bigquery.dataEditor)
**Examples:**

>>> import bigframes.pandas as bpd
>>> import datetime
>>> bpd.options.display.progress_bar = None

Turning an arbitrary python function into a BigQuery managed python udf:

>>> bq_name = datetime.datetime.now().strftime("bigframes_%Y%m%d%H%M%S%f")
>>> @bpd.udf(dataset="bigfranes_testing", name=bq_name)
... def minutes_to_hours(x: int) -> float:
... return x/60

>>> minutes = bpd.Series([0, 30, 60, 90, 120])
>>> minutes
0 0
1 30
2 60
3 90
4 120
dtype: Int64

>>> hours = minutes.apply(minutes_to_hours)
>>> hours
0 0.0
1 0.5
2 1.0
3 1.5
4 2.0
dtype: Float64

To turn a user defined function with external package dependencies into
a BigQuery managed python udf, you would provide the names of the
packages (optionally with the package version) via `packages` param.

>>> bq_name = datetime.datetime.now().strftime("bigframes_%Y%m%d%H%M%S%f")
>>> @bpd.udf(
... dataset="bigfranes_testing",
... name=bq_name,
... packages=["cryptography"]
... )
... def get_hash(input: str) -> str:
... from cryptography.fernet import Fernet
...
... # handle missing value
... if input is None:
... input = ""
...
... key = Fernet.generate_key()
... f = Fernet(key)
... return f.encrypt(input.encode()).decode()

>>> names = bpd.Series(["Alice", "Bob"])
>>> hashes = names.apply(get_hash)

You can clean-up the BigQuery functions created above using the BigQuery
client from the BigQuery DataFrames session:

>>> session = bpd.get_global_session()
>>> session.bqclient.delete_routine(minutes_to_hours.bigframes_bigquery_function)
>>> session.bqclient.delete_routine(get_hash.bigframes_bigquery_function)

Args:
input_types (type or sequence(type), Optional):
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